AI Article Synopsis
- The review highlights the importance of 2-DE (two-dimensional gel electrophoresis) in proteomics for protein separation and analysis, despite newer, less complex methods being available.
- It emphasizes that post-experimental variability, often overlooked, plays a significant role in the overall variability of 2-DE results, affecting both software usage and operator handling.
- The authors provide insights into the sources of error and offer recommendations on how to minimize them, while also discussing the pros and cons of different approaches to improve result quality.
Article Abstract
In the field of proteomics, several approaches have been developed for separating proteins and analyzing their differential relative abundance. One of the oldest, yet still widely used, is 2-DE. Despite the continuous advance of new methods, which are less demanding from a technical standpoint, 2-DE is still compelling and has a lot of potential for improvement. The overall variability which affects 2-DE includes biological, experimental, and post-experimental (software-related) variance. It is important to highlight how much of the total variability of this technique is due to post-experimental variability, which, so far, has been largely neglected. In this short review, we have focused on this topic and explained that post-experimental variability and source of error can be further divided into those which are software-dependent and those which are operator-dependent. We discuss these issues in detail, offering suggestions for reducing errors that may affect the quality of results, summarizing the advantages and drawbacks of each approach.
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| http://dx.doi.org/10.1007/s00726-011-0873-7 | DOI Listing |
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